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In this paper, the authors implement and determine the success the eHealth adoption for queue management when it was first deployed for a community hospital setting in Thailand. The electronic queue system was first implemented to improve conventional operations; then extensive evaluations were conducted to measure the effectiveness for each stakeholder. The healthcare staff shared a common perception that the new system could reduce their workload and increase the efficacy of queue fairness. The overall patient satisfaction and actual waiting time patients spent at the nurse interview station improved significantly. The majority of the patients agreed that the notification for attention from the computerized system is more effective. The community healthcare has strong potential to adopt the eHealth system. Being more automated enabled a reduced burden of administration jobs and significantly reduced waiting times for patients. Patients responded that they had greater satisfaction after the introduction of the electronic queue system.

BackgroundParasitic helminths are highly diverse and ubiquitously distributed in various environments and hosts. Their vast species diversity renders morphological and molecular species delimitation challenging, due to phenotypic and genotypic variations. Currently used approaches to species delimitation are generally computationally intensive. Here, using genetic distances, we developed and validated a simple and easy-to-use application, Applying taxonomic Boundaries for species Identification (ABIapp), to aid in helminth species delimitation.Methodology/Principal FindingsABIapp uses a database of cut-off genetic distances obtained using the K-means algorithm to determine helminth taxonomic boundaries for ten genetic markers: The nuclear 18S and 28S rRNA genes, ITS1 and 2 regions, and the mitochondrial 12S and 16S rRNA, COI, COII, cytB, and ND1 genes. ABIapp was written in R, and the Shiny framework was used to produce an interactive and user-friendly interface. ABIapp requires just three types of input (genetic distance, genetic marker, helminth group) that are easily generated through basic morphological and molecular analysis. To validate ABIapp’s accuracy and robustness for use, validation was performed both in silico and with actual specimens. Prior to validation, ABIapp’s database of genetic distances and species used was increased to broaden the app’s applicability. In silico validation was conducted by obtaining 534 genetic distances from 91 publications and inputting these into ABIapp. Using confusion matrices, an overall classification accuracy of 79% was achieved, demonstrating the robustness and accuracy of ABIapp. Using sequences of the 12S, 16S, COI, and 18S rRNA genes obtained from ten representative helminth specimens, an overall classification accuracy of 75% was achieved.Conclusions/SignificanceOur results demonstrate the applicability and robustness of ABIapp for helminth species delimitation using ten common genetic markers. With a user-friendly interface, coupled with minimal and simple data input and robust classification accuracy, ABIapp provides helminth researchers with a convenient tool for helminth species delimitation.Author summarySpecies delimitation of organisms is often an issue of debate, with varying criteria used to determine species boundaries. Helminths are no exception, and their vast species diversity renders species delimitation challenging due to both physical and genetic variations. Moreover, as climate change progresses, helminths are also adapting to the changing environment through morphological and molecular changes. These variations render it challenging for helminthologists to determine whether a particular helminth belongs to the same or a different species. We have developed an application, ABIapp, a simple tool to aid helminth species delimitation using genetic distances; this app is readily available for a wide audience. Encompassing ten genetic markers for the three parasitic helminth groups (nematodes, trematodes, and cestodes), ABIapp uses cut-off genetic distances generated via machine learning to define species boundaries at each taxonomic level. To use ABIapp, just three types of information are needed, requiring only basic morphological and molecular expertise. We validated ABIapp using both mathematically modeled genetic distances and actual specimens and demonstrated a classification accuracy of 79% and 75%, respectively. This new, convenient, and validated application for helminth species delimitation will aid species identification applicable in the fields of helminth taxonomy, disease diagnosis, and biodiversity.

Previous mathematical models characterising the pharmacokinetic and pharmacodynamic properties of artemisinins in-vivo have not accounted satisfactorily for observed dose-response relationships. We present a new mathematical model of antimalarial pharmacodynamics which incorporates the hypothesis that parasites enter a transitional unresponsive state after contact with artemisinins, followed either by delayed death or reactivation. The model predictions are consistent with both in vitro and in vivo study results.

Background: It is increasingly interesting to monitor pain severity in elderly individuals by applying machine learning models. In previous studies, OpenFace© - a well-known automated facial analysis algorithm, was used to detect facial action units (FAUs) that initially need long hours of human coding. However, OpenFace© developed from the dataset that dominant young Caucasians who were illicit pain in the lab. Therefore, this study aims to evaluate the accuracy and feasibility of the model using data from OpenFace© to classify pain severity in elderly Asian patients in clinical settings.Methods: Data from 255 Thai individuals with chronic pain were collected at Chiang Mai Medical School Hospital. The phone camera recorded faces for 10 seconds at a 1-meter distance briefly after the patients provided self-rating pain severity. For those unable to self-rate, the video was recorded just after the move, which illicit pain. The trained assistant rated each video clip for the Pain Assessment in Advanced Dementia (PAINAD). The classification of pain severity was mild, moderate, or severe. OpenFace© process video clip into 18 FAUs. Five classification models were used, including logistic regression, multilayer perception, naïve Bayes, decision tree, k-nearest neighbors (KNN), and support vector machine (SVM). Results: Among the models that included only FAU described in the literature (FAUs 4, 6, 7, 9, 10, 25, 26, 27 and 45), multilayer perception yielded the highest accuracy of 50%. Among the machine learning selection features, the SVM model for FAU 1, 2, 4, 7, 9, 10, 12, 20, 25, 45, and gender yielded the best accuracy of 58%. Conclusion: Our open-source automatic video clip facial action unit analysis experiment was not robust for classifying elderly pain. Retraining facial action unit detection algorithms, enhancing frame selection strategies, and adding pain-related functions may improve the accuracy and feasibility of the model.

Objective This study aimed to assess the cost-effectiveness of COVID-19 vaccines, preferred COVID-19 vaccine profiles, and the preferred vaccination strategies in Thailand. Methods An age-structured transmission dynamic model was developed based on key local data to evaluate economic consequences, including cost and health outcome in terms of life-years (LYs) saved. We considered COVID-19 vaccines with different profiles and different vaccination strategies such as vaccinating elderly age groups (over 65s) or high-incidence groups, i.e. adults between 20 and 39 years old who have contributed to more than 60% of total COVID-19 cases in the country thus far. Analyses employed a societal perspective in a 1-year time horizon using a cost-effectiveness threshold of 160,000 THB per LY saved. Deterministic and probabilistic sensitivity analyses were performed to identify and characterize uncertainty in the model. Results COVID-19 vaccines that block infection combined with social distancing were cost-saving regardless of the target population compared to social distancing alone (with no vaccination). For vaccines that block infection, the preferred (cost-effective) strategy was to vaccinate the high incidence group. Meanwhile, COVID-19 vaccines that reduces severity (including hospitalization and mortality) were cost-effective when the elderly were vaccinated, while vaccinating the high-incidence group was not cost-effective with this vaccine type. Regardless of vaccine type, higher vaccination coverage, higher efficacy, and longer protection duration were always preferred. More so, vaccination with social distancing measures was always preferred to strategies without social distancing. Quarantine-related costs were a major cost component affecting the cost-effectiveness of COVID-19 vaccines. Conclusion COVID-19 vaccines are good value for money even in a relatively low-incidence and low-mortality setting such as Thailand, if the appropriate groups are vaccinated. The preferred vaccination strategies depend on the type of vaccine efficacy. Social distancing measures should accompany a vaccination strategy.

Background Genetic markers are employed widely in molecular studies, and their utility depends on the degree of sequence variation, which dictates the type of application for which they are suited. Consequently, the suitability of a genetic marker for any specific application is complicated by its properties and usage across studies. To provide a yardstick for future users, in this study we assess the suitability of genetic markers for molecular systematics and species identification in helminths and provide an estimate of the cut-off genetic distances per taxonomic level. Methods We assessed four classes of genetic markers, namely nuclear ribosomal internal transcribed spacers, nuclear rRNA, mitochondrial rRNA and mitochondrial protein-coding genes, based on certain properties that are important for species identification and molecular systematics. For molecular identification, these properties are inter-species sequence variation; length of reference sequences; easy alignment of sequences; and easy to design universal primers. For molecular systematics, the properties are: average genetic distance from order/suborder to species level; the number of monophyletic clades at the order/suborder level; length of reference sequences; easy alignment of sequences; easy to design universal primers; and absence of nucleotide substitution saturation. Estimation of the cut-off genetic distances was performed using the ‘K-means’ clustering algorithm. Results The nuclear rRNA genes exhibited the lowest sequence variation, whereas the mitochondrial genes exhibited relatively higher variation across the three groups of helminths. Also, the nuclear and mitochondrial rRNA genes were the best possible genetic markers for helminth molecular systematics, whereas the mitochondrial protein-coding and rRNA genes were suitable for molecular identification. We also revealed that a general gauge of genetic distances might not be adequate, using evidence from the wide range of genetic distances among nematodes. Conclusion This study assessed the suitability of DNA genetic markers for application in molecular systematics and molecular identification of helminths. We provide a novel way of analyzing genetic distances to generate suitable cut-off values for each taxonomic level using the ‘K-means’ clustering algorithm. The estimated cut-off genetic distance values, together with the summary of the utility and limitations of each class of genetic markers, are useful information that can benefit researchers conducting molecular studies on helminths. Graphical Abstract

Thailand is facing the dilemma of which groups to prioritise for the limited first tranche of vaccinations in 2021. A mathematical modelling analysis was performed to compare the potential short-term impact of allocating the available doses to either the high-risk group (over 65-year-olds) or the high incidence group (aged 20-39). Vaccinating the high incidence group with a vaccine with sufficiently high protection against infection (more than 50%) could provide enough herd effects to delay the expected epidemic peak, resulting in fewer deaths within the 12-month time horizon compared to vaccinating the same number of the high-risk group. After 12 months, if no further vaccination or other interventions were deployed, this strategy would lead to more deaths. With the right vaccine efficacy profile, targeting the high incidence groups could be a viable short-term component of the Thai vaccination strategy. These results and emerging evidence on vaccines and susceptibility suggest prioritisation guidelines should be more nuanced.

Malaria remains at the forefront of scientific research and global political and funding agendas. Malaria models have consistently oversimplified how mass interventions are implemented. Here, we present an individual based, spatially explicit model of P. falciparum malaria transmission that includes all the programmatic implementation details of mass drug administration (MDA) campaigns. We uncover how the impact of MDA campaigns is determined by the interaction between implementation logistics, patterns of human mobility and how transmission risk is distributed over space. Our results indicate that malaria elimination is only realistically achievable in settings with very low prevalence and can be hindered by spatial heterogeneities in risk. In highly mobile populations, accelerating MDA implementation increases likelihood of elimination; if populations are more static, deploying less teams would be cost optimal. We conclude that mass drug interventions can be an invaluable tool towards malaria elimination in low endemicity areas, specifically when paired with effective vector control.

Background Electrocardiographic QT interval prolongation is the most widely used risk marker for ventricular arrhythmia potential and thus an important component of drug cardiotoxicity assessments. Several antimalarial medicines are associated with QT interval prolongation. However, interpretation of electrocardiographic changes is confounded by the coincidence of peak antimalarial drug concentrations with recovery from malaria. We therefore reviewed all available data to characterise the effects of malaria disease and demographic factors on the QT interval in order to improve assessment of electrocardiographic changes in the treatment and prevention of malaria. Methods and findings We conducted a systematic review and meta-analysis of individual patient data. We searched clinical bibliographic databases (last on August 21, 2017) for studies of the quinoline and structurally related antimalarials for malaria-related indications in human participants in which electrocardiograms were systematically recorded. Unpublished studies were identified by the World Health Organization (WHO) Evidence Review Group (ERG) on the Cardiotoxicity of Antimalarials. Risk of bias was assessed using the Pharmacoepidemiological Research on Outcomes of Therapeutics by a European Consortium (PROTECT) checklist for adverse drug events. Bayesian hierarchical multivariable regression with generalised additive models was used to investigate the effects of malaria and demographic factors on the pretreatment QT interval. The meta-analysis included 10,452 individuals (9,778 malaria patients, including 343 with severe disease, and 674 healthy participants) from 43 studies. 7,170 (68.6%) had fever (body temperature ≥ 37.5°C), and none developed ventricular arrhythmia after antimalarial treatment. Compared to healthy participants, patients with uncomplicated falciparum malaria had shorter QT intervals (−61.77 milliseconds; 95% credible interval [CI]: −80.71 to −42.83) and increased sensitivity of the QT interval to heart rate changes. These effects were greater in severe malaria (−110.89 milliseconds; 95% CI: −140.38 to −81.25). Body temperature was associated independently with clinically significant QT shortening of 2.80 milliseconds (95% CI: −3.17 to −2.42) per 1°C increase. Study limitations include that it was not possible to assess the effect of other factors that may affect the QT interval but are not consistently collected in malaria clinical trials. Conclusions Adjustment for malaria and fever-recovery–related QT lengthening is necessary to avoid misattributing malaria-disease–related QT changes to antimalarial drug effects. This would improve risk assessments of antimalarial-related cardiotoxicity in clinical research and practice. Similar adjustments may be indicated for other febrile illnesses for which QT-interval–prolonging medications are important therapeutic options., Xin Hui Chan and co-workers report an individual patient data meta-analysis on disease-specific heart rhythm alterations in malaria., Author summary Why was this study done? Prolongation of the electrocardiographic (ECG) QT interval is a widely used marker of the risk of developing abnormal heart rhythms, which can cause sudden death. Several antimalarial drugs are associated with QT interval prolongation, motivating systematic ECG safety monitoring in many malaria clinical studies. Malaria illness itself may also affect the heart and QT interval, but these disease effects are not well-understood. Interpretation of ECG changes after antimalarial treatment is complicated by the coincidence of peak antimalarial drug concentrations with malaria recovery. What did the researchers do and find? We performed a systematic review and meta-analysis of individual patient data to investigate the effect of malaria disease factors on the ECG QT interval. We included pretreatment data from 10,452 individuals (9,778 malaria patients and 674 healthy participants) from 43 studies in 20 countries. Malaria was associated with QT interval shortening and increased sensitivity of the QT interval to heart rate changes, and these effects increased with malaria severity. Body temperature increase (fever) was associated independently with clinically significant QT shortening. What do these findings mean? To improve cardiac safety assessments of antimalarial medicines, adjustment for malaria recovery- and defervescence-related QT interval prolongation is needed to avoid misattributing QT changes solely to drugs. These findings are relevant to drug developers, healthcare providers, clinical trialists, and policymakers who use QT interval safety data to decide which antimalarials to develop and use and at what dose. Further study of the QT interval in other infections and inflammatory disorders that cause fever and are treated with QT-prolonging medicines is recommended in order to understand the role of disease factors in QT interval changes.

Background The number of Plasmodium falciparum malaria cases around the world has decreased substantially over the last 15 years, but with the spread of resistance against anti-malarial drugs and insecticides, this decline may not continue. There is an urgent need to consider alternative, accelerated strategies to eliminate malaria in countries like Lao PDR, where there are a few remaining endemic areas. A deterministic compartmental modelling tool was used to develop an integrated strategy for P. falciparum elimination in the Savannakhet province of Lao PDR. The model was designed to include key aspects of malaria transmission and integrated control measures, along with a user-friendly interface. Results Universal coverage was the foundation of the integrated strategy, which took the form of the deployment of community health workers who provided universal access to early diagnosis, treatment and long-lasting insecticidal nets. Acceleration was included as the deployment of three monthly rounds of mass drug administration targeted towards high prevalence villages, with the addition of three monthly doses of the RTS,S vaccine delivered en masse to the same high prevalence sub-population. A booster dose of vaccine was added 1 year later. The surveillance-as-intervention component of the package involved the screening and treatment of individuals entering the simulated population. Conclusions In this modelling approach, the sequential introduction of a series of five available interventions in an integrated strategy was predicted to be sufficient to stop malaria transmission within a 3-year period. These interventions comprised universal access to early diagnosis and adequate treatment, improved access to long-lasting insecticidal nets, three monthly rounds of mass drug administration together with RTS,S vaccination followed by a booster dose of vaccine, and screening and treatment of imported cases. Electronic supplementary material The online version of this article (10.1186/s12936-017-2130-3) contains supplementary material, which is available to authorized users.

BackgroundRespiratory syncytial virus (RSV) is the major viral cause of infant and childhood lower respiratory tract disease worldwide. Defining the optimal target product profile (TPP) is complicated due to a wide range of possible vaccine properties, modalities and an incomplete understanding of the mechanism of natural immunity. We report consensus population level impact projections based on two mathematical models applied to a low income setting.MethodTwo structurally distinct age-specific deterministic compartmental models reflecting uncertainty associated with the natural history of infection and the mechanism by which immunity is acquired and lost were constructed. A wide range of vaccine TPPs were explored including dosing regime and uptake, and effects in the vaccinated individual on infectiousness, susceptibility, duration of protection, disease severity and interaction with maternal antibodies and natural induced immunity. These were combined with a range of vaccine implementation strategies, targeting the highest priority age group and calibrated using hospitalization data from Kilifi County Hospital, Kenya.FindingsBoth models were able to reproduce the data. The impact predicted by the two models was qualitatively similar across the range of TPPs, although one model consistently predicted higher impact than the other. For a proposed realistic range of scenarios of TPP combinations, the models predicted up to 70% reduction in hospitalizations in children under five years old. Vaccine designs which reduced the duration and infectiousness of infection were predicted to have higher impacts. The models were sensitive to the coverage and rate of loss of vaccine protection but not to the interaction between vaccine and maternal/naturally acquired immunity.ConclusionThe results suggest that vaccine properties leading to reduced virus circulation by lessening the duration and infectiousness of infection upon challenge are of major importance in population RSV disease control. These features should be a focus for vaccine development.

Malaria remains at the forefront of scientific research and global political and funding agendas. Previous malaria models of mass-interventions have consistently oversimplified how mass interventions are implemented. We present an individual based, spatially explicit model of malaria transmission that includes all the programmatic implementation details of mass drug administration (MDA) campaigns. We uncover how the impact of MDA campaigns is determined by the interaction between implementation logistics, patterns of human mobility and how transmission risk is distributed over space. This translates into a higher likelihood of malaria elimination for areas with true prevalence under 3% with a faster implementation, in highly mobile populations. If populations are more static, deploying less interventions teams would be cost optimal and predicted to be as impactful. We conclude that mass drug interventions can be an invaluable tool towards malaria elimination in the right context, specifically when paired with effective vector control.

Artemisinin resistance constitutes a major threat to the continued success of control programs for malaria. With alternative antimalarial drugs not yet available, improving our understanding of how artemisinin-based drugs act and how resistance manifests is essential to enable optimisation of dosing regimens in order to prolong the lifespan of current first-line treatment options. Here, through introduction of a novel model of the dynamics of the parasites' response to drug, we explore how artemisinin-based therapies may be adjusted to maintain efficacy and how artemisinin resistance may manifest and be overcome. We introduce a dynamic mathematical model, extending on the traditional pharmacokinetic-pharmacodynamic framework, to capture the time-dependent development of a stress response in parasites. We fit the model to in vitro data and establish that the parasites' stress response explains the recently identified complex interplay between drug concentration, exposure time and parasite viability. Our model demonstrates that the previously reported hypersensitivity of early ring stage parasites of the 3D7 strain to dihydroartemisinin (DHA) is primarily due to the rapid development of stress, rather than any change in the maximum achievable killing rate. Of direct clinical relevance, we demonstrate that the complex temporal features of artemisinin action observed in vitro have a significant impact on predictions of in vivo parasite clearance using PK-PD models. Given the important role that such models play in the design and evaluation of clinical trials for alternative drug dosing regimens, our model contributes an enhanced predictive platform for the continued efforts to minimise the burden of malaria., 24 Pages, 9 figures, 2 tables

Additional file 1. Â Model structure and equations.

Background: The Asia-Pacific region has made significant progress in combatting malaria since 2000 and a regional goal for a malaria-free Asia Pacific by 2030 has been recognised at the highest levels. External financing has recently plateaued and with competing health risks, countries face the risk of withdrawal of funding as malaria is perceived as less of a threat. An investment case was developed to provide economic evidence to inform policy and increase sustainable financing. Methods: A dynamic epidemiological-economic model was developed to project rates of decline to elimination by 2030 and determine the costs for elimination in the Asia-Pacific region. The compartmental model was used to capture the dynamics of Plasmodium falciparum and Plasmodium vivax malaria for the 22 countries in the region in a metapopulation framework. This paper presents the model development and epidemiological results of the simulation exercise. Results: The model predicted that all 22 countries could achieve Plasmodium falciparum and Plasmodium vivax elimination by 2030, with the People’s Democratic Republic of China, Sri Lanka and the Republic of Korea predicted to do so without scaling up current interventions. Elimination was predicted to be possible in Bangladesh, Bhutan, Malaysia, Nepal, Philippines, Timor-Leste and Vietnam through an increase in long-lasting insecticidal nets (and/or indoor residual spraying) and health system strengthening, and in the Democratic People’s Republic of Korea, India and Thailand with the addition of innovations in drug therapy and vector control. Elimination was predicted to occur by 2030 in all other countries only through the addition of mass drug administration to scale-up and/or innovative activities. Conclusions: This study predicts that it is possible to have a malaria-free region by 2030. When computed into benefits and costs, the investment case can be used to advocate for sustained financing to realise the goal of malaria elimination in Asia-Pacific by 2030.

The emergence of artemisinin-resistant Plasmodium falciparum malaria is a major threat to malaria elimination. New tools for supporting the surveillance of artemisinin resistance are critical for current and future malaria control and elimination strategies. We have developed an open-access, user-friendly, web-based tool to analyse parasite clearance half-life data of P. falciparum infected patients after treatment with artemisinin derivatives, so that resistance to artemisinin can be identified. The tool can be accessed at bit.ly/id_artemisinin_resistance.

Artemisinin-resistant Plasmodium falciparum malaria has emerged in western Cambodia. Resistance is characterized by prolonged in vivo parasite clearance times (PCTs) following artesunate treatment. The biological basis is unclear. The hypothesis that delayed parasite clearance results from a stage-specific reduction in artemisinin sensitivity of the circulating young asexual parasite ring stages was examined. A mathematical model was developed, describing the intrahost parasite stage-specific pharmacokinetic–pharmacodynamic relationships. Model parameters were estimated using detailed pharmacokinetic and parasite clearance data from 39 patients with uncomplicated falciparum malaria treated with artesunate from Pailin (western Cambodia) where artemisinin resistance was evident and 40 patients from Wang Pha (northwestern Thailand) where efficacy was preserved. The mathematical model reproduced the observed parasite clearance for each patient with an accurate goodness of fit (rmsd: 0.03–0.67 in log 10 scale). The parameter sets that provided the best fits with the observed in vivo data consist of a highly conserved concentration–effect relationship for the trophozoite and schizont parasite stages, but a variable relationship for the ring stages. The model-derived assessment suggests that the efficacy of artesunate on ring stage parasites is reduced significantly in Pailin. This result supports the hypothesis that artemisinin resistance mainly reflects reduced ring-stage susceptibility and predicts that doubling the frequency of dosing will accelerate clearance of artemisinin-resistant parasites.

Background Unprecedented efforts are underway to eliminate malaria. Mathematical modelling can help to determine the optimal strategies for malaria elimination in different epidemiological settings. This is necessary as there is limited scope for expensive and time-consuming field studies and failure of planned elimination strategies is likely to discourage ongoing investment by funders. However, there has been very little modelling of malaria elimination and little direct involvement of policymakers in its development. There is thus an urgent need for user-friendly and accessible models purpose-designed in collaboration with policymakers to answer pertinent questions arising from the field. Results An internet site is presented with a simple mathematical modelling platform for population level models of malaria elimination. It is freely accessible to all and designed to be flexible so both the platform and models can be developed through interaction with users. The site is an accessible introduction to modelling for a non-mathematical audience, and lessons learned from the project will help inform future development of mathematical models and improve communication of modelling results. Currently it hosts a simple model of strategies for malaria elimination and this will be developed, and more models added, over time. The iterative process of feedback and development will result in an educational and planning tool for non-modellers to assist with malaria elimination efforts worldwide. Conclusions By collaboration with end users, iterative development of mathematical models of malaria elimination through this internet platform will maximize its potential as an educational and public health policy planning tool. It will also assist with preliminary optimisation of local malaria elimination strategies before commitment of valuable resources.

Understanding the evolution of drug resistance in malaria is a central area of study at the intersection of evolution and medicine. Antimalarial drug resistance is a major threat to malaria control and directly related to trends in malaria attributable mortality. Artemisinin combination therapies (ACT) are now recommended worldwide as first line treatment for uncomplicated malaria, and losing them to resistance would be a disaster for malaria control. Understanding the emergence and spread of antimalarial drug resistance in the context of different scenarios of antimalarial drug use is essential for the development of strategies protecting ACTs. In this study, we review the basic mechanisms of resistance emergence and describe several simple equations that can be used to estimate the probabilities of de novo resistance mutations at three stages of the parasite life cycle: sporozoite, hepatic merozoite and asexual blood stages; we discuss the factors that affect parasite survival in a single host in the context of different levels of antimalarial drug use, immunity and parasitaemia. We show that in the absence of drug effects, and despite very different parasite numbers, the probability of resistance emerging at each stage is very low and similar in all stages (for example per-infection probability of 10(-10)-10(-9) if the per-parasite chance of mutation is 10(-10) per asexual division). However, under the selective pressure provided by antimalarial treatment and particularly in the presence of hyperparasitaemia, the probability of resistance emerging in the blood stage of the parasite can be approximately five orders of magnitude higher than in the absence of drugs. Detailed models built upon these basic methods should allow us to assess the relative probabilities of resistance emergence in the different phases of the parasite life cycle.

Background Preventing the emergence of anti-malarial drug resistance is critical for the success of current malaria elimination efforts. Prevention strategies have focused predominantly on qualitative factors, such as choice of drugs, use of combinations and deployment of multiple first-line treatments. The importance of anti-malarial treatment dosing has been underappreciated. Treatment recommendations are often for the lowest doses that produce "satisfactory" results. Methods The probability of de-novo resistant malaria parasites surviving and transmitting depends on the relationship between their degree of resistance and the blood concentration profiles of the anti-malarial drug to which they are exposed. The conditions required for the in-vivo selection of de-novo emergent resistant malaria parasites were examined and relative probabilities assessed. Results Recrudescence is essential for the transmission of de-novo resistance. For rapidly eliminated anti-malarials high-grade resistance can arise from a single drug exposure, but low-grade resistance can arise only from repeated inadequate treatments. Resistance to artemisinins is, therefore, unlikely to emerge with single drug exposures. Hyperparasitaemic patients are an important source of de-novo anti-malarial drug resistance. Their parasite populations are larger, their control of the infection insufficient, and their rates of recrudescence following anti-malarial treatment are high. As use of substandard drugs, poor adherence, unusual pharmacokinetics, and inadequate immune responses are host characteristics, likely to pertain to each recurrence of infection, a small subgroup of patients provides the particular circumstances conducive to de-novo resistance selection and transmission. Conclusion Current dosing recommendations provide a resistance selection opportunity in those patients with low drug levels and high parasite burdens (often children or pregnant women). Patients with hyperparasitaemia who receive outpatient treatments provide the greatest risk of selecting de-novo resistant parasites. This emphasizes the importance of ensuring that only quality-assured anti-malarial combinations are used, that treatment doses are optimized on the basis of pharmacodynamic and pharmacokinetic assessments in the target populations, and that patients with heavy parasite burdens are identified and receive sufficient treatment to prevent recrudescence.

Background Artemisinin combination therapy (ACT) is now the recommended first-line treatment for falciparum malaria throughout the world. Initiatives to eliminate malaria are critically dependent on its efficacy. There is recent worrying evidence that artemisinin resistance has arisen on the Thai-Cambodian border. Urgent containment interventions are planned and about to be executed. Mathematical modeling approaches to intervention design are now integrated into the field of malaria epidemiology and control. The use of such an approach to investigate the likely effectiveness of different containment measures with the ultimate aim of eliminating artemisinin-resistant malaria is described. Methods A population dynamic mathematical modeling framework was developed to explore the relative effectiveness of a variety of containment interventions in eliminating artemisinin-resistant malaria in western Cambodia. Results The most effective intervention to eliminate artemisinin-resistant malaria was a switch of treatment from artemisinin monotherapy to ACT (mean time to elimination 3.42 years (95% CI 3.32–3.60 years). However, with this approach it is predicted that elimination of artemisinin-resistant malaria using ACT can be achieved only by elimination of all malaria. This is because the various forms of ACT are more effective against infections with artemisinin-sensitive parasites, leaving the more resistant infections as an increasing proportion of the dwindling parasite population. Conclusion Containment of artemisinin-resistant malaria can be achieved by elimination of malaria from western Cambodia using ACT. The "last man standing" is the most resistant and thus this strategy must be sustained until elimination is truly achieved.

Unprecedented efforts are now underway to eliminate malaria from many regions. Despite the enormous financial resources committed, if malaria elimination is perceived as failing it is likely that this funding will not be sustained. It is imperative that methods are developed to use the limited data available to design site-specific, cost-effective elimination programmes. Mathematical modelling is a way of including mechanistic understanding to use available data to make predictions. Different strategies can be evaluated much more rapidly than is possible through trial and error in the field. Mathematical modelling has great potential as a tool to guide and inform current elimination efforts. Economic modelling weighs costs against characterised effects or predicted benefits in order to determine the most cost-efficient strategy but has traditionally used static models of disease not suitable for elimination. Dynamic mathematical modelling and economic modelling techniques need to be combined to contribute most effectively to ongoing policy discussions. We review the role of modelling in previous malaria control efforts as well as the unique nature of elimination and the consequent need for its explicit modelling, and emphasise the importance of good disease surveillance. The difficulties and complexities of economic evaluation of malaria control, particularly the end stages of elimination, are discussed. © 2010 Royal Society of Tropical Medicine and Hygiene.

For many countries, as Professor Greenwood points out, the answer to his question is a resounding ‘yes’.1 Perhaps the more pertinent question is, with so many more interventions now available, ‘how?’. The two most important new tools that have been added to our malaria control arsenal since the 1950s are artemisinin combination therapies and insecticide-treated bed nets. These are most likely to be effective when used together, in combination with indoor residual spraying where there are suitable vectors. The problem we are faced with is a lack of experience of using such an elimination strategy on a large scale, leading to a lack of data, particularly on the best way to roll out ACTs. In the past, mass screening and treatment, mass drug administration and large-scale replacement of first-line therapies have all been attempted, with varying degrees of success. In the context of newly arisen artemisinin resistance and the diminishing effectiveness of the pyrethroid insecticides, particular care must be taken to preserve the effectiveness of these compounds for as long as possible, whilst achieving maximum impact in a wide variety of epidemiological settings. Another powerful tool that we did not have in the 1950s is sophisticated computer-based mathematical modelling. This facilitates the use of the limited data currently available to make predictions about future events. In other words, it provides a rational framework on which to base decisions made using limited but diverse and complex inter-related information, such as population demographics, pharmacokinetics and pharmacodynamics, treatment-seeking behaviour, spatially distributed risk factors, the presence of antimalarial resistance, etc. Identifying, preparing and calibrating such data sets for each country for the application of mathematical models is a huge challenge in itself.2 The accuracy of modelling predictions improves in an iterative process as more data become available. Hence, the more intense the malaria elimination efforts are and as long as data are rigorously collected, the more useful and informative modelling becomes. The Bill and Melinda Gates Foundation and others have realized the potential of mathematical modelling to help guide malaria elimination and eradication. Malaria elimination/eradication modelling is still in its infancy (a PubMed search for mathematical modelling papers with the keywords ‘malaria’, ‘elimination’ or ‘eradication’ produced only two articles)3,4 so these groups have provided significant funding to focus modelling efforts on this problem. Professor Greenwood states that new tools are likely to be required in order to eliminate malaria in high-transmission areas.1 If, eventually, we do have the luxury of an effective malaria vaccine or powerful new gametocytocidal drugs they are likely to be expensive and their scale-up will inevitably be based on limited trial data in the initial stages. Mathematical modelling will be indispensible in helping to maximize their impact.

Background Malaria has recently been identified as a candidate for global eradication. This process will take the form of a series of national eliminations. Key issues must be considered specifically for elimination strategy when compared to the control of disease. Namely the spread of drug resistance, data scarcity and the adverse effects of failed elimination attempts. Mathematical models of various levels of complexity have been produced to consider the control and elimination of malaria infection. If available, detailed data on malaria transmission (such as the vector life cycle and behaviour, human population behaviour, the acquisition and decay of immunity, heterogeneities in transmission intensity, age profiles of clinical and subclinical infection) can be used to populate complex transmission models that can then be used to design control strategy. However, in many malaria countries reliable data are not available and policy must be formed based on information like an estimate of the average parasite prevalence. Methods A simple deterministic model, that requires data in the form of a single estimate of parasite prevalence as an input, is developed for the purpose of comparison with other more complex models. The model is designed to include key aspects of malaria transmission and integrated control. Results The simple model is shown to have similar short-term dynamic behaviour to three complex models. The model is used to demonstrate the potential of alternative methods of delivery of controls. The adverse effects on clinical infection and spread of resistance are predicted for failed elimination attempts. Since elimination strategies present an increased risk of the spread of drug resistance, the model is used to demonstrate the population level protective effect of multiple controls against this very serious threat. Conclusion A simple model structure for the elimination of malaria is suitable for situations where data are sparse yet strategy design requirements are urgent with the caveat that more complex models, populated with new data, would provide more information, especially in the long-term.